| name | size | modified_date | id |
|---|---|---|---|
| Preprocess.R | 2.6000e+03 | 06/02/2024 10:43 PM | syn60236613 |
| Sample_annotation.csv | 8.9500e+04 | 05/30/2024 8:37 AM | syn60157686 |
| Probe_array.csv | 7.3000e+07 | 05/30/2024 9:04 AM | syn60157718 |
| Probe_annotation.csv | 5.6870e+08 | 05/30/2024 8:49 AM | syn60157694 |
| DetectionP_subchallenge1.csv | 2.7340e+09 | 05/24/2024 5:35 AM | syn59870646 |
| DetectionP_subchallenge2.csv | 4.9870e+09 | 05/24/2024 5:51 AM | syn59872208 |
| Beta_raw_subchallenge1.csv.gz | 5.8690e+09 | 05/24/2024 5:19 AM | syn59868755 |
| Beta_raw_subchallenge2.csv.gz | 1.1008e+10 | 05/24/2024 2:19 PM | syn59898399 |
| GEO Number | N |
|---|---|
| GSE128827 | 5 |
| GSE228149 | 5 |
| GSE200659 | 11 |
| E_MTAB_9312 | 13 |
| GSE74738 | 13 |
| GSE108567 | 16 |
| GSE75196 | 24 |
| GSE115508 | 25 |
| GSE98224 | 48 |
| GSE69502 | 52 |
| GSE204977 | 55 |
| GSE169598 | 64 |
| GSE100197 | 95 |
| GSE232778 | 187 |
| GSE144129 | 210 |
| GSE167885 | 242 |
| GSE75248 | 334 |
| GSE71678 | 343 |
| V1 | V2 |
|---|---|
| anencephaly | chorioamnionitis |
| anencephaly | diandric_triploid |
| anencephaly | gdm |
| anencephaly | hellp |
| anencephaly | ivf |
| anencephaly | lga |
| anencephaly | miscarriage |
| anencephaly | spina_bifida |
| anencephaly | subfertility |
| diandric_triploid | chorioamnionitis |
| diandric_triploid | hellp |
| diandric_triploid | ivf |
| diandric_triploid | lga |
| diandric_triploid | sga |
| diandric_triploid | subfertility |
| fgr | anencephaly |
| fgr | ivf |
| fgr | subfertility |
| gdm | diandric_triploid |
| ivf | chorioamnionitis |
| ivf | hellp |
| ivf | subfertility |
| miscarriage | chorioamnionitis |
| miscarriage | hellp |
| miscarriage | ivf |
| miscarriage | sga |
| miscarriage | subfertility |
| ms_ivf | anencephaly |
| ms_ivf | diandric_triploid |
| ms_ivf | ivf |
| ms_ivf | miscarriage |
| ms_ivf | ms_subfertility |
| ms_ivf | spina_bifida |
| ms_ivf | subfertility |
| ms_subfertility | chorioamnionitis |
| ms_subfertility | hellp |
| ms_subfertility | ivf |
| ms_subfertility | subfertility |
| pe | anencephaly |
| pe | diandric_triploid |
| pe | hellp |
| pe | pe_onset |
| pe | spina_bifida |
| pe_onset | anencephaly |
| pe_onset | diandric_triploid |
| pe_onset | hellp |
| pe_onset | ivf |
| pe_onset | miscarriage |
| pe_onset | spina_bifida |
| pe_onset | subfertility |
| preterm | diandric_triploid |
| preterm | ivf |
| preterm | miscarriage |
| preterm | subfertility |
| sga | lga |
| spina_bifida | chorioamnionitis |
| spina_bifida | diandric_triploid |
| spina_bifida | gdm |
| spina_bifida | hellp |
| spina_bifida | ivf |
| spina_bifida | lga |
| spina_bifida | miscarriage |
| spina_bifida | subfertility |
| subfertility | chorioamnionitis |
| subfertility | hellp |
## $fgr
##
## $pe
##
## $pe_onset
##
## $preterm
##
## $anencephaly
##
## $spina_bifida
##
## $gdm
##
## $diandric_triploid
##
## $miscarriage
##
## $lga
##
## $subfertility
##
## $hellp
##
## $chorioamnionitis
## $ivf
##
## $subfertility
Normal-GA model was trained using samples without 12 of 13 available conditions. They were significantly correlated to GA: (1) fetal growth restriction (FGR); (2) PE; (3) PE onset (early/late/not applicable); (4) hemolysis, elevated liver enzyme, and low platelet (HELLP) syndrome; (5) anencephaly; (6) spina bifida; (7) diandric triploid; (8) miscarriage; (9) preterm delivery; (10) gestational diabetes mellitus (GDM); (11) large-for-gestational-age (LGA) infant; (12) subfertility; and (13) chorioamnionitis. We excluded preterm delivery because it was related to the outcome, i.e., GA, simply by definition.
Res-Conds-GA model was similar to Resfull-GA model but we used predictors of multiplication values for each predicted probability and residual GA estimated by a model for the corresponding condition. Specifically, we trained a model using beta values of DMPs among samples with a condition. The rationale was that the conditions have different trajectories of when pregnancies are terminated and each pregnant woman has a different set of probabilities of the conditions. We used the predicted probabilities of the conditions from the prediction (Resfull-GA) model for the conditions.
Res-Comb- model was considered because other conditions might affect pregnancy termination, not limited to the 12 conditions. In Res-Comb-GA model, we limited the degree of freedom of residual fitting using known phenotype information (Res-Conds-GA), thus, the second model only fitted the unexplained residual GA (Res-CPG-GA), simply to boost the prediction. Res-Comb-GA model consisted of three models for <37, ≥37 and ≤40, and >40 weeks’ gestation estimated by normal-GA model. The model numbers and periods were also determined according to clinical knowledge and pursuing normal distribution of residual GA. The estimated delivery date falls on 40 week’s gestation. Before this date, a pregnant woman might seek termination in advance due to a medical condition. Meanwhile, a normal pregnant woman might seek for termination since the delivery date. We used three approaches, i.e., predicting residual GA during: (1) <37 weeks’ gestation only (Res-Comb-PR-GA); (2) both <37 and ≥37 and ≤40 weeks’ gestation, i.e., term before the estimated delivery date (Res-Comb-PRTB-GA); and (3) all the three periods (Res-Comb-GA).
Since some probes in sub-challenge were imputed, the model accuracy would be lower using the same pipeline (Figure 3). We did not refine the probe imputation techniques. It is because our question specifically asked whether correcting collider-restriction bias added a substantial improvement in placental clock accuracy. In this subchallenge, we also used Res-Comb-GA model which was trained using 450k probes.
| model | metric | avg | lb | ub | current_best | win | sub | code | rank | task | val |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Normal-GA | RMSE | 3.004 | 2.994 | 3.014 | 0.956 | No | |||||
| Normal-GA | MAE | 1.935 | 1.927 | 1.943 | 0.721 | No | |||||
| Normal-GA | r | 0.921 | 0.921 | 0.922 | 0.976 | No | |||||
| Normal-GA (450k) | RMSE | 1.859 | 1.851 | 1.866 | 0.956 | No | |||||
| Normal-GA (450k) | MAE | 1.142 | 1.137 | 1.148 | 0.721 | No | |||||
| Normal-GA (450k) | r | 0.967 | 0.967 | 0.968 | 0.976 | No | |||||
| Resfull-GA* | RMSE | 0.738 | 0.736 | 0.740 | 0.956 | Yes | |||||
| Resfull-GA* | MAE | 0.497 | 0.496 | 0.499 | 0.721 | Yes | |||||
| Resfull-GA* | r | 0.994 | 0.994 | 0.994 | 0.976 | Yes | |||||
| Resfull-GA* (450k) | RMSE | 0.946 | 0.941 | 0.950 | 0.956 | Yes | 2 | isitthedarkhorse | 6 | 1 | 1.3552 |
| Resfull-GA* (450k) | MAE | 0.613 | 0.611 | 0.615 | 0.721 | Yes | 2 | isitthedarkhorse | 6 | 1 | 1.073 |
| Resfull-GA* (450k) | r | 0.990 | 0.990 | 0.990 | 0.976 | Yes | 2 | isitthedarkhorse | 6 | 1 | 0.9505 |
| Res-Conds-GA§ | RMSE | 0.896 | 0.893 | 0.898 | 0.956 | Yes | |||||
| Res-Conds-GA§ | MAE | 0.631 | 0.630 | 0.633 | 0.721 | Yes | |||||
| Res-Conds-GA§ | r | 0.991 | 0.991 | 0.991 | 0.976 | Yes | |||||
| Res-Conds-GA§ (450k) | RMSE | 0.819 | 0.817 | 0.822 | 0.956 | Yes | 7 | slidingdoors | 2 | 1 | 1.0949 |
| Res-Conds-GA§ (450k) | MAE | 0.551 | 0.550 | 0.553 | 0.721 | Yes | 7 | slidingdoors | 2 | 1 | 0.8843 |
| Res-Conds-GA§ (450k) | r | 0.992 | 0.992 | 0.992 | 0.976 | Yes | 7 | slidingdoors | 2 | 1 | 0.9642 |
| Res-Comb-GA | RMSE | 0.701 | 0.699 | 0.703 | 0.956 | Yes | |||||
| Res-Comb-GA | MAE | 0.496 | 0.495 | 0.498 | 0.721 | Yes | |||||
| Res-Comb-GA | r | 0.994 | 0.994 | 0.994 | 0.976 | Yes | |||||
| Res-Comb-GA (450k) | RMSE | 0.568 | 0.566 | 0.569 | 0.956 | Yes | 8 | pointofdivergence | 1 | 1 | 1.0772 |
| Res-Comb-GA (450k) | MAE | 0.389 | 0.388 | 0.390 | 0.721 | Yes | 8 | pointofdivergence | 1 | 1 | 0.8876 |
| Res-Comb-GA (450k) | r | 0.996 | 0.996 | 0.996 | 0.976 | Yes | 8 | pointofdivergence | 1 | 1 | 0.9663 |
## $`Normal-GA`
##
## $`Normal-GA (450k)`
##
## $`Resfull-GA*`
##
## $`Resfull-GA* (450k)`
##
## $`Res-Conds-GA§`
##
## $`Res-Conds-GA§ (450k)`
##
## $`Res-Comb-GA`
##
## $`Res-Comb-GA (450k)`
You need to generate all files that are required for the task 1
submission 2. Then, copy all pcd1_sub2/data and
pcd1_sub2/inst/extdata in task 1 to
pcd2_sub1/data and pcd2_sub1/inst/extdata in
task 2.
You need to generate all files that are required for the task 1
submission 7. Then, copy all pcd1_sub7/data and
pcd1_sub7/inst/extdata in task 1 to
pcd2_sub2/data and pcd2_sub2/inst/extdata in
task 2.